Radars are well known in the prior art for determining the range of targets. For operation, radars send an electromagnetic pulse, which hits an object and comes back. But to obtain a correct range to the target, the target must be within the maximum unambiguous range of the radar, in order for the bounced signal to be received before the next pulse is sent. More specifically, suppose a type of radar sends a pulse every T μsecs. In order for the return signal to be received before the next pulse is sent, the round trip time must be less than T μsecs. Thus, the maximum unambiguous range, D, for the radar can be defined in terms of this time value, T and speed of light.
The time between the start of consecutive radar transmissions or electromagnetic pulses can be defined in the prior art as the Pulse Repetition Interval (PRI), also called the pulse period or ranging interval. When a radar emission is evaluated, estimation of the emission parameters and characterization when compared to other radars can be based on analysis of the radar PRI information from the intercepted return pulse emission. Thus, PRI can be a good starting point to characterize a radar based on the intercepted emission. But PRI by itself is typically not enough for a good characterization of the emission (and the radar it came from). To increase the level of confidence in the characterization of the emission, other parameters need to be considered. These other parameters can include the radar clock, crystal, mode and countdowns.
But the characterization of PRIs, clocks, crystals, modes and countdowns can require the consideration of a massive amount of data, and might even be impossible for an analyst to accomplish by oneself. What is needed is a system that can automatically characterize radars using numerous radar parametric modeling techniques, that can rapidly process live data, and that can reduce the time, number of complex tools, and manual operations that analysts currently perform to complete their daily tasks. Such a system would allow analysts to focus their efforts on evaluating and verifying the resulting models, instead of focusing on sorting through tremendous amounts of data.
In view of the above, an object of the present invention can be to provide systems and methods for radar characterization and model formation that automate the signals intelligence (SIGINT) manual processes on board maritime, land, and air platforms, in order to rapidly characterize previously unknown radars. Yet another object of the present invention can be to provide systems and methods for radar characterization and model formation that automatically correlate contact reports to form tracks, that estimate values for unknown radar parameters (clock, crystal, mode, and countdowns), and that group tracks with similar parameters to form comprehensive models for different types of radar. Still another object of the present invention can be to provide systems and methods for radar characterization and model formation that function as an analyst multiplier by reducing the analysis process from hours to seconds, to enable real-time processing and analysis of worldwide data. Another object of the present invention can be to provide systems and methods for radar characterization and model formation that can automatically identify and combine similar tracks from around the world to produce robust radar parameter models that may not be discoverable by a single analyst conducting manual processes. Another object of the present invention to provide systems and methods for radar characterization and model formation that can be implemented in a cost-effective manner.